JSTOR Daily

Efficient Bayesian Hierarchical Functional Data Analysis with Basis Function Approximations Using Gaussian-Wishart Processes

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order ...
Ars Technica

Optimizing matrix code once functional

I've finally hit the point where getting a program running is not enough. I have a functional Gaussian process and need to speed it up. Most of the time is used up by a conjugate gradient descent with ...
Nature

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots.

The Nature Index 2025 Research Leaders — previously known as Annual Tables — reveal the leading institutions and countries/territories in the natural and health sciences, according to their output in ...
JSTOR Daily

Locally Adaptive Bayes Nonparametric Regression via Nested Gaussian Processes

We propose a nested Gaussian process (nGP) as a locally adaptive prior for Bayesian nonparametric regression. Specified through a set of stochastic differential equations (SDEs), the nGP imposes a ...